In generating a complete digital model of a multidimensional object, such as a varied geographic terrain, it is necessary to combine several overlapping range images (also known as frames or volumes of data points) taken from different perspectives. The frames are pieced together using a process known as registration to produce a multidimensional model of the object. In a registration process the required translation and rotation of the different frames is determined. This process uses six parameters: Δx, Δy, Δz, Δω, Δκ, and Δø, where the first three parameters relate to the translation of the respective x, y, and z coordinates and the second three parameters, each relate to rotation on each of the three respective x, y, and z axes. Known methods of registering frames comprising large numbers of data points from different and overlapping perspectives of an object are computationally intensive and hence time consuming. However, many applications of the registration technology require a fast response from the time that the frames are acquired. Therefore, there is a need for a faster and more robust system for registering multidimensional data points.
Known methods of topographical point collection include imaging Laser RADAR (LIDAR) and IFSAR (Interferometric Synthetic Aperture Radar). Referring to FIG. 1, there is shown an example of an airborne LIDAR system 100. The system 100 comprises a LIDAR instrument 102 mounted on the bottom of an aircraft 104. Below the aircraft is an area comprising a ground surface partially obscured by a canopy formed by trees and other foliage obstructing the view of the ground (earth) from an aerial view. The LIDAR instrument 102 emits a plurality of laser light pulses which are directed toward the ground. The instrument 102 comprises a sensor 103 that detects the reflections/scattering of the pulses. The LIDAR instrument 102 provides data including elevation versus position information from a single image. It should be noted however, that multiple frames or portions of the area from different perspectives are used to generate the image. The tree canopy overlying the terrain also results in significant obscuration of targets (e.g. tank 106) under the tree canopy. The points received by the sensor 103 of instrument 102 from the ground and the target 106 are thus sparse. Hence, a robust system for processing the points is required in order to generate an accurate three-dimensional image. Moreover, to be of the most tactical and strategic value, an image of the ground wherein the target 106 can be perceived easily must be available quickly.